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Large language models (LLMs) have show great ability in various natural language tasks.
Fpga implementation of md5 hash algorithm
Janaka Deepakumara, Howard M Heys, and R Venkatesan. 2001 · 2001
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Natural language processing with Python: analyzing text with the natural language toolkit
Steven Bird, Ewan Klein, and Edward Loper. 2009 · 2009
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A tutorial on kernel density estimation and recent advances
Yen-Chi Chen. 2017 · 2017
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Defending against neural fake news
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019 · 2019
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020 · 2020
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Array programming with NumPy
Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. van Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández del Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke, and Travis E. Oliphant. 2020 · 2020
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On the sentence embeddings from pre-trained language models
Bohan Li, Hao Zhou, Junxian He, Mingxuan Wang, Yiming Yang, and Lei Li. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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OPUS-MT — Building open translation services for the World
Jörg Tiedemann and Santhosh Thottingal. 2020 · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola. 2020 · 2020
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Simcse: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
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Declutr: Deep contrastive learning for unsupervised textual representations
John Giorgi, Osvald Nitski, Bo Wang, and Gary Bader. 2021 · 2021
Cited alongside, same era.
Selection-inference: Exploiting large language models for interpretable logical reasoning
Antonia Creswell, Murray Shanahan, and Irina Higgins. 2022 · 2022
Cited alongside, same era.
All the news that’s fit to fabricate: Ai-generated text as a tool of media misinformation
Sarah Kreps, R Miles McCain, and Miles Brundage. 2022 · 2022
Cited alongside, same era.
Learn to explain: Multimodal reasoning via thought chains for science question answering
Pan Lu, Swaroop Mishra, Tanglin Xia, Liang Qiu, Kai-Wei Chang, Song-Chun Zhu, Oyvind Tafjord, Peter Clark, and Ashwin Kalyan. 2022 · 2022
Cited alongside, same era.
Creating and detecting fake reviews of online products
Joni Salminen, Chandrashekhar Kandpal, Ahmed Mohamed Kamel, Soon-gyo Jung, and Bernard J Jansen. 2022 · 2022
Cited alongside, same era.
Origin tracing and detecting of llms
Linyang Li, Pengyu Wang, Ke Ren, Tianxiang Sun, and Xipeng Qiu. 2023 · 2023
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Detectgpt: Zero-shot machine-generated text detection using probability curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D Manning, and Chelsea Finn. 2023 · 2023
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Can ai-generated text be reliably detected?
Vinu Sankar Sadasivan, Aounon Kumar, Sriram Balasubramanian, Wenxiao Wang, and Soheil Feizi. 2023 · 2023
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The science of detecting llm-generated texts
Ruixiang Tang, Yu-Neng Chuang, and Xia Hu. 2023 · 2023
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Gpt-who: An information density-based machine-generated text detector
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
Cited alongside, same era.
A systematic evaluation of large language models of code
Frank F Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn. 2022 · 2022
Cited alongside, same era.
Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, et al. 2022 · 2022
Cited alongside, same era.
How close is chatgpt to human experts? comparison corpus, evaluation, and detection
Biyang Guo, Xin Zhang, Ziyuan Wang, Minqi Jiang, Jinran Nie, Yuxuan Ding, Jianwei Yue, and Yupeng Wu. 2023 · 2023
Cited alongside, same era.
Challenges and applications of large language models
Jean Kaddour, Joshua Harris, Maximilian Mozes, Herbie Bradley, Roberta Raileanu, and Robert McHardy. 2023 · 2023
Cited alongside, same era.
Paraphrasing evades detectors of ai-generated text, but retrieval is an effective defense
Kalpesh Krishna, Yixiao Song, Marzena Karpinska, John Wieting, and Mohit Iyyer. 2023 · 2023
Cited alongside, same era.
Robust distortion-free watermarks for language models
Rohith Kuditipudi, John Thickstun, Tatsunori Hashimoto, and Percy Liang. 2023 · 2023
Cited alongside, same era.
Saranya Venkatraman, Adaku Uchendu, and Dongwon Lee. 2023 · 2023
Closest in time.
Seqxgpt: Sentence-level ai-generated text detection
Pengyu Wang, Linyang Li, Ke Ren, Botian Jiang, Dong Zhang, and Xipeng Qiu. 2023 · 2023
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A survey on llm-gernerated text detection: Necessity, methods, and future directions
Junchao Wu, Shu Yang, Runzhe Zhan, Yulin Yuan, Derek F Wong, and Lidia S Chao. 2023 · 2023
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On the generalization of training-based chatgpt detection methods
Han Xu, Jie Ren, Pengfei He, Shenglai Zeng, Yingqian Cui, Amy Liu, Hui Liu, and Jiliang Tang. 2023 · 2023
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Provable robust watermarking for ai-generated text
Xuandong Zhao, Prabhanjan Ananth, Lei Li, and Yu-Xiang Wang. 2023 · 2023
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Copyright protection in generative ai: A technical perspective
Jie Ren, Han Xu, Pengfei He, Yingqian Cui, Shenglai Zeng, Jiankun Zhang, Hongzhi Wen, Jiayuan Ding, Hui Liu, Yi Chang, et al. 2024 · 2024
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Robust multi-bit natural language watermarking through invariant features
KiYoon Yoo, Wonhyuk Ahn, Jiho Jang, and Nojun Kwak. 2023 · 2092
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